SPATIO-TEMPORAL CLUSTERING of MOVEMENT DATA: AN APPLICATION to TRAJECTORIES GENERATED by HUMAN-COMPUTER INTERACTION

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 1, Issue , 2012, Pages 147-152

  McArdle, G ^a   Tahir, A ^b   Bertolotto, M ^b  

^a NATIONAL UNIVERSITY OF IRELAND   (Ireland)

^b UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

Data mining; spatial; tasks; visualisation; web based

Indexed keywords

EID: 84991200469     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprsannals-I-2-147-2012     Document Type: Conference Paper

References (33)

1. Altiparmak, F., Ferhatosmanoglu, H., Erdal, S. and Trost, D., 2006. Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases. IEEE Transactions on Information Technology in Biomedicine 10(2), pp. 254-263.
2. Andrienko, G., Andrienko, N. and Voss, H., 2003. Gis for everyone: the commongis project and beyond. Maps and the Internet. Elsevier Science pp. 131-146.
3. Andrienko, G., Andrienko, N. and Wrobel, S., 2007. Visual analytics tools for analysis of movement data. ACM SIGKDD Explorations Newsletter 9(2), pp. 38-46.
4. Ankerst, M., Breunig, M., Kriegel, H. and Sander, J., 1999. Optics: Ordering points to identify the clustering structure. ACM SIGMOD Record 28(2), pp. 49-60.
5. Atev, S., Miller, G. and Papanikolopoulos, N., 2010. Clustering of vehicle trajectories. Intelligent Transportation Systems, IEEE Transactions on 11(3), pp. 647-657.
6. Chen, L., Ö zsu, M. and Oria, V., 2005. Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, ACM, pp. 491-502.
7. Chen, W., Song, Y., Bai, H., Lin, C. and Chang, E., 2011. Parallel spectral clustering in distributed systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(3), pp. 568-586.
8. Dodge, S., Weibel, R. and Forootan, E., 2009. Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems 33(6), pp. 419-434.
9. Doyle, J., Bertolotto, M. and Wilson, D., 2010. Evaluating the benefits of multimodal interface design for compass-a mobile gis. Geoinformatica 14(2), pp. 135-162.
10. Eccles, R., Kapler, T., Harper, R. and Wright, W., 2007. Stories in geotime. In: Visual Analytics Science and Technology, 2007. VAST 2007. IEEE Symposium on, IEEE, pp. 19-26.
11. Hägerstrand, T., 1970. What about people in regional science? Papers in Regional Science 24(1), pp. 6-21.
12. Han, J., 2005. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
13. Jain, A., Murty, M. and Flynn, P., 1999. Data clustering: a review. ACM Computing Surveys (CSUR) 31(3), pp. 264-323.
14. Jensen, C., Lu, H. and Yang, B., 2009. Indexing the trajectories of moving objects in symbolic indoor space. Advances in Spatial and Temporal Databases pp. 208-227.
15. Jeung, H., Yiu, M. L. and S. Jensen, C., 2011. Trajectory pattern mining. In: Y. Zheng and X. Zhou (eds), Computing with Spatial Trajectories, Springer-Verlag New York Inc, pp. 144-145.
16. Laube, P., Imfeld, S. and Weibel, R., 2005. Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science 19(6), pp. 639-668.
17. Lee, J.-G., Han, J. and Li, X., 2008. Trajectory outlier detection: A partition-and-detect framework. In: Proceedings of the International Conference on Data Engineering (ICDE), pp. 140-149.
18. Lee, J., Han, J. and Whang, K., 2007. Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, ACM, pp. 593-604.
19. MacAoidh, E., Bertolotto, M. and Wilson, D., 2008. Understanding geospatial interests by visualizing map interaction behavior. Information Visualization 7(3-4), pp. 275.
20. McArdle, G. and Demsar, U., 2011. Investigating similarity of trajectories through physical decomposition of movement in a space-time cube. In: 14th AGILE International Conference on Geographic Information Science, Advancing Geoinformation Science for a Changing World.
21. Morris, B. and Trivedi, M., 2009. Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009., pp. 312-319.
22. Panagiotakis, C., Pelekis, N., Kopanakis, I., Ramasso, E., Theodoridis, Y. et al., 2011. Segmentation and sampling of moving object trajectories based on representativeness. IEEE Transactions on Knowledge and Data Engineering 1, pp. 17.
23. Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N. and Andrienko, G., 2008. Visually driven analysis of movement data by progressive clustering. Information Visualization 7(3), pp. 225-239.
24. Sakurai, Y., Yoshikawa, M. and Faloutsos, C., 2005. Ftw: fast similarity search under the time warping distance. In: Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, ACM, pp. 326-337.
25. Song, Y., Chen, W., Bai, H., Lin, C. and Chang, E., 2008. Parallel spectral clustering. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, pp. 374-389.
26. Spretke, D., Janetzko, H. and Mansmann, F., 2011. Exploration through enrichment: A visual analytics approach for animal movement. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, pp. 421-424.
27. Tahir, A., McArdle, G. and Bertolotto, M., 2011. A web-based visualisation tool for analysing mouse movements to support map personalisation. In: Proceedings of the First International Workshop on Spatial Information Modeling, Management and Mining (SIM3) (in conjunction with DASFAA2011), LNCS, Springer, pp. 132-143.
28. Tahir, A., McArdle, G. and Bertolotto, M., 2012. Identifying specific spatial tasks through clustering and geovisual analysis. In: Proceedings of Geoinformatics 2012, IEEE GRSS. in press.
29. Thomas, J. and Cook, K., 2005. Illuminating the path: The research and development agenda for visual analytics. Vol. 54, IEEE.
30. Vlachos, M., Kollios, G. and Gunopulos, D., 2002. Discovering similar multidimensional trajectories. In: Proceedings of 18th International Conference on Data Engineering, IEEE, pp. 673-684.
31. Vrotsou, K., Forsell, C. and Cooper, M., 2010. 2d and 3d representations for feature recognition in time geographical diary data. Information Visualization 9(4), pp. 263.
32. Xu, R., Wunsch, D. et al., 2005. Survey of clustering algorithms. Neural Networks, IEEE Transactions on 16(3), pp. 645-678.
33. Zheng, Y. and Zhou, X., 2011. Computing with Spatial Trajectories. Springer-Verlag New York Inc.